The Data Team's Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot

Now what you need is, and that's where our agent spa comes into play. Spotter is like your personal AI analyst, your data analyst, where you can ask any questions. I'll tell you how I start my, how today I started my day, it's the middle of the week and I'm tracking my top deals for my quarter, right?

I'm, as A-C-E-O-I kind of do that, right? How we progressing on the top deals. I asked Potter, Hey, show me the latest on the top deals and give me your sentiment. And it's, my first deal is already closed. The second is happening third. It flagged as yellow or red or something like that, right?

And I'm like, great. And it said that there's a lack of engagement and activity. And then I said that kind of seems odd. My sales team was telling me it's a hot account, like what's happening? But then I'm, then I asked Potter why don't you look at all data sources and come back to me with it?

Look at Slack also. And then spotter, not just went to structured data, went to my Slack data, connected to the channel, figured it out, joined all of it, and told me, you know what, I think, so there's a lot of slack activity happening. We see this happening. So are you seeing this? Like I could get my answers.

I'm the CEO, but I don't have a data person. I don't have an ops team. Who's my operations team? Spotter my agent. That's my operations team. But that's just one simple use case where I have truly become self-service, if you may, because we run our staff meeting like that. We run a forecast call like that.

But now you can multiply this across use case after use case who doesn't have questions because that's the second important point about BI or analytics. You get an answer. You have one more question. Then you have one more question and it's like that question and answer Tango. And in the olden days I'm, I actually am calling it olden days because it is, it just feels like an olden day in the olden days.

Back in those days, every follow-up question would either become a filter on the top. Have you seen those dashboards with like many filters on the top? What are those filters? They're basically following questions people have, right? And you have to select it. But spotter, you can just have a very engaging conversation because spotter is not just your analyst, it's also your brainstorming partner.

So that's an example of one. I'll give you another example, which I think so is way more exciting than this is a lot of people have a lot of data, but let's. Let's, and we all say it's messy. It's all over the place. How do you get your data ready for the AI world? The biggest limiting factor to adoption is not the intent of embracing ai, but it is data readiness.

It is having your data in a governed fashion metrics semantic model. That's why we have another agent spotter model. And spotter model is like your data engineer agent. So now your data engineers can just tell spotter to to do all the semantic automated semantics for their data. And now your data is becoming AI ready times faster.

We have then done gone ahead and we said, but yeah, people will still need dashboards sometimes, right? It's not like you don't need a dashboard you need some charts for your qbr, for your presentations, for your board meetings, blah, blah, blah, blah, blah, blah, all that stuff. Or every day.

You don't wanna start your day with just asking a question. You probably wanna start with a chart and then go from there. Which is fair. That's why we created Spotter Wizz. Spotter. Wizz is like lovable for dashboards. You, it'll just create your dashboards for you. So now imagine this world where in every persona, every in the analytics workflow can be augmented or rather amplified.

Your impact can be amplified with agents. I think agents make you a super team. And I'll tell you, Richie, everybody is gonna have five of these agents working for you. You're gonna have them working for you, Richie you need your own data analyst, Richie, and you need that by seven and that's your agent.

So we are very excited about what's happening in the world right now and we are taking it one step further. In a, in the next couple of months, we are getting ready to launch. The working name for it is Agent Spot. It's a, but I'm sure my CMO will have something to say about it and I'll let her think about it.

I've got a wonderful CMO but the idea there is not to stop at there. The idea is to take it from insights to automate actions and outcomes. That is the world of autonomous analytics. So we are gonna move. So we are in agentic today and from agent, we are gonna move to autonomous which is gonna be a whole another set of innovations we can talk about.

Richie Cotton: Absolutely. There's a lot of exciting things to talk about, so I'll forgive you a long answer there. But it's interesting. So you talked about, self-service analytics being a hoax, and I guess it's maybe been an aspirational thing for a decade that we're gradually getting closer to now.

All the agents you mentioned they sound like employee replacements almost. So I'm just working trying to work out how close are we to having an agent that is equivalent to a real employee and where a human still useful. 

Ketan Karkhanis: No Richie, I may have to take a different perspective out there.

I don't think so. There are replacements. I'll tell you the biggest problem in the data team has been a shortage of talent. Every company has highly constrained data teams. Have you seen their backlogs? If you go to a data team and you ask them to show them your backlog to be like my. They're wonderful people.

They're really smart people, but no human being can go through all of that. Why? Because the data needs of an organization are in finite. It's a long tail of data needs. So what happens? You take your two people and you put them on the most important one, and what happens to the rest? Eh? They get ignored.

Not because you don't want to do them, not because you don't like those people. It's just you. You physically, you can't get to it. This is the very important point. What we are seeing is customers are coming to us and they're saying, whoa, for the first time in my life, I can actually get through my backlog and keep my entire business stakeholder team happy.

This is the idea of having an augmented super team, is the data analyst plus their agent spotter ways. It is the data engineer plus their agent spotter model. I think so that's how we think about it. Now over time, will certain things that an analyst or an engineer or a business user do themselves be not needed to be done?

Yeah, absolutely. So for example, I'll give you a great example. A lot of times analysts are spending time on dashboard layout and structure and charts and this and that, but they're an analyst. They're not a dashboard creator. See we have made analysts need to analyze the data and give you strategic inputs and help you think through your business.

So actually it takes time. It frees them up from the mundane, allowing them to really focus on the business context and the strategy over time. I think. So it's a massive, what I call force. Agents are your force multiplier. So if you are a data team, you're like, whoa, I can now do x more. Who doesn't wanna do x more?

Richie Cotton: Yeah, I'm sure everyone wants to do x more. And I do take your point, because I've spoken to people in a lot of different data teams in different companies and there's no one who's yeah, we're having a slack week. We've not gone much work to do. Yeah. Everyone in this industry is busy.

So I like the idea that, yeah, more productivity is definitely needed and it's not necessarily gonna be like overcut jobs because agents have taken over, I do you, you do make it sound like the roles are changing though. Perhaps do you wanna start with data analyst? Are you looking for different skill profiles?

Are people doing different tasks? How does the data analyst role change now that you have powerful agents? I think so. It's the most exciting time to be a data analyst in the industry right now. I genuinely believe so, because for a change, they're no longer just churning out dashboards. They don't like that all day long.

Ketan Karkhanis: They're just churning out dashboards. That's not fun. Oh, add a filter. Great. I'm feeling very accomplished. I added a filter to it. They don't like that, but we make them do that. So number one, I think so AI helps them actually have a extremely amazingly fulfilling career. That's number one. But let's take that real persona.

What changes for them? Two things change for them. Number one is they absolutely have to embrace new skills. And that's why we are investing a lot. We have our community the ThoughtSpot Champions community, where we are helping data analysts embrace the new world of ai. And new dimensions start getting added to their job.

And I think, so this is still getting figured out. So let's not say everything is set in stone. There's a little bit of learning we all have to do. And I think, so it's important to be humble about it. Like we, some stuff we need to discover still, right? But I'll tell you the big thing we are seeing data analysts do now is becoming AI stewards.

They're becoming stewards of the data governance semantics. Two. Their role is not about, now I give you five dashboards, but I give you these curated covered data sets, which then you can do anything you want with. But three, they also become agent architects. They become agent whisperers. They are the AI enablers.

For your data platform. Of course that will require a little bit of new skills and a little bit of exciting new things to learn. But I think it's like the best time to be a data analyst, right? I think so. It's the best time to be in the data business period, 

Richie Cotton: actually. Yeah. It, I'm agree with you that it's definitely exciting times to be a data analyst.

You mentioned the idea of curating the semantics of the data so that, requires some domain knowledge then to understand what does the data actually mean? So is there a shift then a data analyst becoming closer to commercial teams then? 

Ketan Karkhanis: Oh two things. Yes. No, absolutely. I think so this does allow analysts and data engineers to come closer to their quote unquote business teams, right?

And really ingrained themselves in in, in the business of the business versus the technology of the business. And I think, so that's a very important strategic point to make. But two I think so. What's also happening is they can now use AI or spot model, in this case to automate semantics. Like semantics is hard manual work.

Right column descriptions, metadata metrics, keeping everything needy, organized, and then one thing changes and it has to ripple everywhere else. And then you've got data here with one semantic model data here with, there's a lot of what people have to end up doing. But I think, so this our agents actually help them speed semantics but also that there's a subtle part which is.

Which we are picking up from our customers is the biggest value they're getting is also understanding what is in their data. Do you know one of the biggest problems, and sometimes that's referred to as data literacy and all that kind of stuff, but the favorite question people ask Porter, my we, we pull people on what kind of questions you're asking, what's bubbling up, and you know what.

What's hot, if you may, the hot question, which we see so many customers ask is two questions. Number one is what is in my data? What's in this data? Just tell me what's in it. And then they're asking brainstorming questions like, I want to measure pipeline velocity. How do I do that? So it's less about the what, but it's like the how.

I think so this is very exciting. Very exciting. Because now spotter is becoming your thought partner, your trusted advisor. So I think so it's all these things put together that, that I think so are gonna be in fact next week? I think so. Yeah, I think so. Yeah. Monday, March th or something, we are launching a whole new platform.

We are calling it spotter semantics. And I'm sure we'll make a big splash out of it, but I'm very excited about spotter semantics because that comes included with the hotspot platform. 

Richie Cotton: Okay, wonderful. Yeah. I can certainly see how, being able to have AI that helps you figure out what the methodology should be in order to get the answers is all just equally as useful as just giving you the direct answer and not explaining how did it get there.

Ketan Karkhanis: It also builds trust Richie? Because when I when you when you see how certain things are done, you start trusting your ai because look, there are lots of lots of people out there will be like here's the answer. How do I trust it? How do I know you follow the right thing?

So yeah, it's quite important. 

Richie Cotton: Absolutely. Certainly if you've got some kind of regulatory compliance thing of, if you've gotta comply with any regulations, then you're gonna have to explain how is the analysis done? You don't have just an answer. You've gotta show you're working as well.

Ketan Karkhanis: Yeah. And auditable auditable and traceable, right? Like you should be able to walk back the answer and see exactly how it was arrived. So yeah, absolutely. 

Richie Cotton: Okay, wonderful. And so we talked a bit about how data analyst roles are changing. How about data engineers? Is there a similar sort similar sort of shift there?

Ketan Karkhanis: Yeah, I think so. Again, the same concept is how can they use an AI first data engineering. Approach it's less now or about us writing the SQL or manually updating the metadata and all that. And it's more about like the best part is if you look at SPOT model, our agent for data modeling and data engineering you start using SPOT model by not saying, these are the three tables I have in Snowflake.

You start by saying, my data is here. I want to create a lead performance dashboard. How should I model my data and sport a model? We'll then identify your data and then go through all of it. So I think, so the shift happens on, again, to a very important point you mentioned wherein a data engineer is no longer just a data engineer, but also a business engineer.

They're understanding the business problem, the context, and designing the model to arrive to give the business team. The insights they need. But I see all data engineers to be becoming data AI first. They don't have to now write scripts and all that to update. One of the biggest lacking things in people data is column descriptions.

I know it sounds like a simple thing, but we've observed that everywhere. Everywhere. And it's terrible. It's terrible. I'll tell you why it's terrible because we had designed our world for humans. We have designed data and metadata structures for humans. The biggest shift that is happening is now you have to design it for agents.

Your meta, your column description, should be agent friendly, AI friendly, and not human friendly, because humans are never gonna read that anymore. Agents are gonna read that for you. So that's why it's a very exciting shift that's happening out there. 

Richie Cotton: No, that's fair. I think historically, yeah, humans hate writing documentation.

They hate reading documentation and so yeah it's always lacking. I suppose that's the good thing about that. Yeah. Maybe ai data analyst. Is okay with reading documentation. That's fine. So is that something you need to get set up then before you start making use of agents? Do you need to have all this documentation there in place before you get going with agents?

Ketan Karkhanis: No, we have been working hard on what we call the Cold Start problem. What we are focused on is not just a shiny demo. A very core principle we have is our AI will lead you to ROI. And if it doesn't, then, and then that's the conversation. So to set up or to get started with hotspot? I think so The biggest thing you need is is curiosity.

I'm sure. We love if you are on already a cloud data warehouse, snowflake is an excellent partner of us. Databricks is a great partner of ours, Google BigQuery, great partner of ours. And we are so seamlessly integrated with them. That if you are on Snowflake, you can get started in two minutes.

Because for example, I'll tell you, if you have all your semantic model in Snowflake, ThoughtSpot will just inherit it. You don't have to worry about it. Or if you're using so many other things out there. So yeah, no, to get started, we just encourage people. I think, so this is also an age wherein it's not just about technology, but it's also about imagining a future and having the curiosity to imagine that.

And what we are doing is spending time with our customers, so many of them across the world, whether it's Schneider in, in Europe, whether it's Macquarie Bank in Australia, whether it's EasyJet in London, whether it's NAND right here in the valley down the road. All of them are focused on one thing and one thing only.

How can they use data to transform not just their internal operations, but their customer experience? This is a very big thing that's happening. I was just, before coming to your recording, I was seeing on LinkedIn, somebody posted one of our customers. That's always the best thing for me is that my customers are posting videos of how they're using ThoughtSpot.

That's pretty cool, but Thrive has embedded AI into their own application, transforming their customer experience with our Intelligent Apps products. So it's becoming an imperative. That you transform data is going to be the driver of the AI enterprise. It's gonna be right at the center, and data is gonna be powering not just analytics and insights agent takes, but applications and agents.

And that triangle, at that center of that is where ThoughtSpot is. That's why we are having so much fun, as you can tell. 

Richie Cotton: Yeah, definitely. There's lots of different kind of I guess outputs here. You say you can have agents as the output, you can have applications as the output. So lots of different possibilities there.

You've been working with Snowflake, with Databricks, with big Google per their BigQuery product it sounds like. All this kind of governance. Then it's happening in the data warehouse. So is that the idea that you get all your data warehouse in order and that's gonna enable the use of agents?

Ketan Karkhanis: Yes and no. Both. Like we want to give our customers choices. Every customer is at a different maturity curve, right? Some are at a place wherein they have everything neatly, beautifully organized, and they're good to go, right? Some are like I'm not even in the cloud. Ca. And I need to go. And that's a reality.

Nothing right or wrong about it, it's just you. What we are doing is so for example, a no. No, it's not necessary for you to have everything organized. That's where our tooling and our technology comes into play. We really focus on how can we get you to value in days on one use case. How can we get your first agent live in days in one use case, and then you go from there.

What we are noticing is a lot of customers, once they see that first proof point. Once they see the first use, it just catches like wildfire. Then they don't require any prodding. It's then the tail wags the dog and then the data starts becoming clean very quickly. But no, it's not required that you should have everything organized or anything like that at all.

Not at all. 

Richie Cotton: Data, quality data, cleaning data management is one of our sort of regular topics on the show. And it's it's somehow I've never solved problem. It's like the tooling is getting better, but there's always work to be done there. I'd love to zoom out a little bit and talk about data strategy.

Now we've got all this different tooling just a high level reorganization. Do you need to completely change your data strategy? 

Ketan Karkhanis: I think so. Let's start. Strategy is an important part, but I think so. The first aspect of data strategy is. It gotta be your data culture. And I think so culture becomes the key multiplier.

For whether it's your data strategy or your AI strategy. And what do I mean by that? I'll give you a couple of examples. Just a couple. Not that I'm a, not that I'm gonna give you a culture doctrine out here. But one of the most important things we have seen our customers struggle with is, is is just the enormity of the job. They're like, oh my God, I've got this years of what am I gonna do? And then some of them actually come to us and they say Kitan, I've got like thousands of Tableau dashboards, which nobody even knows what they do. And what do I where It just seems enormous, right?

Getting outta it. But the culture of don't let perfection be the enemy of progress is critical. Absolutely critical. And I think so that becomes center stage leaders who are driving with that culture are making the most amount of progress and achieving the most amount of success in their organizations.

Two is realizing that the, that when you think of your data strategy is more of the same, is not the answer. You can't just say, I'm gonna make. Oh, I've, I'm just going to, my strategy is to make dashboard creation easier, I think. So that's missing the point with utmost you with utmost respect. I, I wanna be very empathetic to, to everybody, but it's like saying, thinking the legacy products out there that, the visualization and all those products, they're almost like the flip phones.

You have to skip the generation and move to a smartphone. You cannot say, I'm now gonna have a flip phone with a slide out keyboard. Is I'm trying to have fun with you right now, but is this making sense? You have to take the org, generational leap, and you have to upgrade. Not it's an upgrade.

Supercycle is what we are seeing. But if customer and a key part of the strategy is no. The ability to talk to your data is now not an option. It is a imperative. The ability to have agents with every business user is an imperative. And how do you do that? And in the beginning, just like back in those days when the smartphone launched, people were like it's only for the exec.

No, everybody's gonna have that. Everybody's gonna need that. So I think so, and the third point I would say when it comes to data strategy is look, recognizing that it's going to be. Not all in one place. We are seeing a lot of our customers with what I call multiple data states. And it's a very important fact that allowing then the ability, for example, one of the capabilities we are working on is having the ability to have a semantic model that transcends multiple data states.

Imagine that like a common understanding layer across data states. So recognizing that is important. Ultimately the key part of the data strategy, and I'm not saying anything technical. If you observe like it's not because I think, so we've got the technology, you gotta bring the culture and together it's then a party.

The final point I'll make out there is people, a key part of your data strategy needs to be your people strategy. How are you gonna organize your data team? What are the new skills you're gonna invest? How are you gonna redo your approach to managing your business stakeholders? And that has to be AI first agent first.

And that is if you think about those things the next year, you are gonna still see, it's like all these things, the progress seems like slow at the beginning, and then it's sudden. And I think, so we are at the tail end of that slow period wherein people are still coming out of it, getting a better appreciation for it, understanding it, and then boom, you're, it's, by next year you're gonna see everybody having their own data agent working with you.

Like you're gonna have one. If not, I'll send you one. 

Richie Cotton: No, I agree with that. Like it's amazing how quickly it's gone from AI agents are coming soon, they are the future to now it's almost table stakes that you have to have. Way of working for for anything with data. Now you asked some great questions there how do you organize your your data team and your organization?

What, how do you upskill all your people? Those are actually gonna be my next question, so I wanna make you answer the, those questions that you just asked. 

Ketan Karkhanis: Which one do you want first? 

Richie Cotton: Okay let's go with the organizational design stuff. 'cause yeah. Do team structures need to change? Do you need different ways of teams interacting with each other?

Like, how should the, all this work? 

Ketan Karkhanis: Oh and we are shaping this up ourselves. So we are working with a lot of, we are working with a group of TopSpot champions. These are key members of our community. As I said, our community is our strength and we are we're, this is another thing, like all my best ideas are not mine.

They're. From my customers. I just wanna be very clear about that. I'm simply a assembler of all of their ideas, if you may. But one of the key things, a great idea they had, and I'll, and maybe it's a small thing, but the first thing you should look at is how are you hiring people? Go look at the job description you have.

Then look in those job description and are the first two bullet points. AI readiness and AI savvy. Like you can use AI tools like remember, I'll give you a very simple example. If you're a data analyst, you probably have it in the job description. Familiar with using SQL and Snowflake and X, Y, Z, and X, Y, Z, right?

Forget that the first point should be savviness with agentic infrastructure. Ability to use, agent first models familiarity with LLMs and everybody will have their own tweaks, but the point is, first, introduce that in your hiring so that you start attracting and building the right teams.

Two, the next thing that we are seeing a lot of our customers do is invest in their people. We can't assume everybody's just gonna learn this on their own. I think so we have to help, we have to have empathy. We have to have, to give them a path to come into the future. We absolutely have to.

And that's why you see I firmly say to everybody is make this training mandatory. Don't make it optional. It is. It's will you hire anybody who. Doesn't know how to use the internet. Sounds like a silly question, 

Richie Cotton: but I suppose. Yeah. A couple of decades ago there was managed mandatory training for, this is how you use the internet.

Ketan Karkhanis: That's the same thing. This is bigger than the internet. Three, I think. Set clear expectations and goals. The big thing we are seeing in some companies is like they get lost. They get enamored with demos. Somebody will come demo them. Hey, I can do text to SQL conversational.

And what they're doing is text to sql. Text to SQL was done years ago. You don't need AI to do text to sql. Okay? Like seriously. But and the reason I say that is connect your AI initiatives to business initiatives and ROI take three, four tangible use cases. I want to do this supply chain.

Nike is a great example that's working with us or Cisco is another great example. So connect it, connect AI to ROI and frankly I would say that be prepared to to experiment together. And I'm not giving you a playbook, but I'm giving you certain ideas. Because that's the very important point.

Don't wait for a baked five year strategy. If you're trying to write down a five year strategy, you are gonna miss the train big time. Because there is, this thing is changing every three months. You wanna be very agile in how we are approaching it. 

Richie Cotton: Absolutely. Yeah. It's very difficult to predict five years in the future.

I think if you go back to no one's gonna have predicted this year very accurately. Okay. You mentioned about the job adver and how you want AI readiness, AI skills right at the top of the list. The more I found out about hiring, like it amazes me how much like job adverts they just copied and pasted from last year and it goes.

Keep, keeps going on the same. So I like the idea of writing something from scratch to sa the skills you want. Would you still keep things like sequel, snowflake, all those things? Are they gonna be in that job advert just laid down or is let's focus on the AI goods? 

Ketan Karkhanis: Yeah, they will be. But look, if you are, if your ability to just write a great sequel is less.

Value, then your ability to transact, to think business, be AI forward, be familiar with using technologies like spotter or spotter model and so on and so forth. And I think, so it's it almost becomes yeah, of course you know how to write sql. Like, why, you know what I'm saying?

Is that, is, that's not the tilting factor for you to get the job, but also you have to think about the workforce of the future. And the workforce of the future is an AI first workforce. It is happening. And we all need to reskill and upskill into that future. We all need to, some will do it sooner, some will do it later but all of us will have to do it.

There's it's just gonna happen. And I'll tell you, it's extremely exciting and rewarding, extremely. I'll tell you one of the best. Yesterday I got one of my our HR leader, Tim. She sends me a text message. I was supposed to talk to her and like we were, so this, we just back and forth.

We were on text and she text me is can you gimme a few more minutes? I'm a little tied up right now. I'm like, sure, what's up? You know what's up? She's no I'm building this agent, which is gonna help people onboard in the company and I wanna experiment with it. Just that conversation she was having with me was incredible.

And she was excited. She was like, whoa, I now have. The power to fundamentally alter not just the employee experience, which is very important to an HR leader, is the employee experience, but also the candidate experience. So from her job fulfillment standpoint, AI is helping her be a much more incredible HR executive and leader.

So what I'm trying to say here, this is a micro example of it's actually gonna help you feel more fulfilled and in control. And in control of what you're doing. So I think so it's gonna be exciting. Embrace it is my mesh is, my mission is. Just message. That's it. 

Richie Cotton: Yeah, I love that story because HR teams, I guess traditionally they're not highly technical people.

You go into a, you go into HR 'cause you love working with people, not necessarily technology. Yeah, this is someone who is using cutting edge technology. They don't necessarily have they're probably not a programmer, but they're doing something which has a high impact, both for you.

Internal employees, but also new employees and candidates. 

Ketan Karkhanis: I'll tell you, building agents is gonna be as ease as, as common as using an Excel file. 

Richie Cotton: That's brilliant. I'm not a huge fan of Excels. I think this is, there's a more for this is a more full of tech. Yeah. But yeah, it's ev it's everywhere.

Wonderful. Alright. I'd like to talk a little bit about change management as well. So how do you get to this state where everyone's adopting ai? Everyone's actually making use of this stuff because. Building those new habits is difficult. 

Ketan Karkhanis: Habits, that's a key word you used out there.

We touched upon some of those in our strategy topic, but the key out here is there are two ways to do change management. One is is just force feeding it in your organization and all that stuff. And let's lead that aside. Some might do it, some may wanna do it. I'll tell you the biggest way to think about change management is number one, leaders need to embody it first.

It's really important if you are a leader, whether you're a data leader, an IT leader, a line of business owner, an executive, A CEO, doesn't matter whoever you are, you need to first show vulnerability by saying, yeah, I, I need to go learn. It's okay. Not everybody's born with AI fed into their veins. It's like some of us have to go learn and it's okay.

And it's okay to say that is my point. And you have to give your organization the permission to do that. So number one is. Live the live, live that AI first value. I'll tell you a great example In my staff, in my leadership, my ELT meeting. Every Thursday I have my ELT meeting. I do it on Thursdays.

I don't do it on Mondays. There's many reasons why some, and that's another podcast. But Thursdays, I do my ELT and the whole meeting is run on. No, you're not allowed. Any slides excels. Nothing is allowed in that meeting. You cannot do that. You are we run the whole meeting on spa. Our agent, like my agent is like an ELT member for me.

Is this making sense? And this agent sitting at the table with me and my lt. So it's my, it's a C-suite. My agent is my C-suite. It's funny. It's really funny because then funny in a very positive way because then you start using it in real situations, not in theoretical demos.

So for example, one of the first ways we start the meeting is we look at our NPS every meeting, every Thursday, we are tracking NPS both by segment, by industry, by many different aspects. But the first question we ask is, how are our customers doing? What is last week's NPS we track it at that micro level.

And my usual question is Potter is and we are like, what was NPS? And then there's a follow on conversation. And then the conversation is somebody will say that happened, so that happened. And then Spotter will say something that this happened and it creates like this interesting dynamic in the beginning.

But very soon at the beginning, people resisted. I saw like my LT was a little bit like slightly uncomfortable if you may. What is spotter gonna say now? What is this AI going to say now? But very soon they love it because everybody's now on the same page and answers are instantaneous.

So living it is important, however, imperfect, however imperfect it might be to begin with. Live with it. Live it. Number three, I would say, for change management, I'll make this point and I cannot make it enough, is is ROI. Every AI project has to be tied to ROI and that ROI could be. We are gonna eliminate Tableau dashboards that have been lying around, and we are maintaining and wasting time, maintaining our.

That ROI could be, we are gonna reduce travel policy cancellations by X amount or that ROI could be, we are gonna drive % supply chain efficiency in this country for this product. Doesn't matter what it could be. Is there, and the final point I would say is change management is be prepared to let go.

It's a subtle point. But be prepared to let go because you can't be in this world of hyper command control data in the organization. What you have to think about is governed center of excellence, spawning innovation of data at the edge. So as long as you have the governed center, let the edge, let a thousand flowers blue, and that will help you figure out which.

Area to focus on, but we could talk change management for hours. I'm not an expert, but I'm just reflecting what I'm seeing in the market. 

Richie Cotton: It's a whole, it's a whole episode in itself. But I do love your idea of agents as being people in a meeting. People's not quite the right word, but this is a concept I've not heard before.

I'd love to know a bit more details about how this works. Does everyone get their own version of Spotter within the meeting, or is it like, is this a voice activation? Join your zoom call on the thing. How's it work? 

Ketan Karkhanis: No, I'm not fully at the Jarvis level yet, though. I would love there.

My team is building voice by the way, so we should have that very soon. No, we have it up on the screen. You know how when you're at a staff meeting, you have a presentation and somebody has a slide with data or some metrics, so no, we just have spot on the screen. That's it. And somebody will, my chief of staff or me, I would just ask a question or anybody will ask a question and then people jump over each other.

Then they're like no. Ask spotter that. No. Ask Spotter that. So it becomes a game, if you may, but and it's not just the idea is to provoke the right conversations. That is the key. % of the time, we as humans, we know what is the right thing to do if we are debating the right things.

And bringing focus to what matters is what Spotter does. And I think so that's really exciting, but that's just one way of using it. I'll tell you another way of using it. My head of we've got an incredible sales leader Scott Parsons, who is like our SVP of North America. He leads all of North, he's a sales leader.

Not exactly a tech first kind of a personality. But I was with him yesterday and he's Caton, here's what I did with Spotter. They all come and tell me here's what they did with Spotter recently. Like it's fun. And he's Caton, I'm taking a trip to New York. So I told Spotter and I was like, figuring out what to do.

Usually I would've asked my admin and everybody to figure out and reach out to my local sales leaders so that they can organize meetings and so on and so forth. But instead, he went to Spotter and he said, I'm traveling to NY slash NJ next week. Look up our customers and tell me whom should I be meeting based on our pipeline?

Richie Cotton: Okay, 

Ketan Karkhanis: nice. Boom. And it looked at the pipeline. It looked at deal progression. It looked at all of that. Then it looked at relevant slack conversations. For those things, knowing which is hard, which is not, and prepared for him, a list of top accounts to visit while he is there. Then he says, okay, great.

Now he is okay, great. Send a message to all the AEs to try and organize meetings for this. And then, but this is an, I'm giving you another example like the examples and numerous, but the idea is what would've taken him, I don't know, emails and phone calls and coordination book. He just did it himself.

Richie Cotton: These are not from a task, like calling lots of people and having backers and vos and things and just organizing that stuff. It's real kind of, unless you're really into administration, which not many people are, these are the tasks you want AI to be taken care of, so you can focus on the more fun stuff.

We talked a lot about the positive of ai. I want to, are there sensible ways of dealing when it makes mistakes? Particularly a lot of these agents that are in their infancy, and if your data's not perfect, then it's gonna make mistakes. So how do you do this safely? 

Ketan Karkhanis: It's a very important point.

I was in the beginning of the meeting, I was asking you, I was telling you what's hot, what's not, what people are saying to spotter. One of the, one of the things that I see a lot of people now I mean I, we teach people to do that is check your work or show me your work. Is this making sense?

Like you literally want to say to spot, show me your work. How did you do this? Or sometimes I say huh, that is odd. Can you double check please? I'd say Please also, because just, I'm just a nice guy. And the idea there is accepting the fact that we are not in a perfect. AI is not perfection.

The job of AI is to give you directional guidance, % there and for you to always have that human in the loop idea, but it's also supported by what I call fundamental architecture choices. And I'll tell you why we can do it and why others can't do it. So in Spaa, when you ask a question and you get an answer, we are not just plopping a chart, we are giving an explanation.

But most importantly, spotter will first show you. How it arrived at the answer, what tables it looked at, what formulas it used, blah, blah, blah, blah, blah, blah, and it'll give you all of that. Second, you can then click edit and actually see the tokens that we are used to generate. The sql. A big part of our technology is we are not using an LLM to generate sql.

It's a very important point, rich. Everybody else is doing that. That's not good. That's not good. So I know you wanna say something, but I'll, the bigger problem is AI and LLMs, by definition are not. Our prob AI is just prop listed. What we are doing is we are using the LLM to convert language into tokens, but from token to SQL to your database, which could be direct query.

We don't create cache. We hit your Snowflake is all our proprietary technology, TML, and our search data technology that we have built, and that allows us to create a trusted, repeatable fashion. The same answer will be the, you'll get the same answer. Plus we should go all the way down to not just show you the sequel we generated, but the query path and the query plan that we created.

So there's a lot of transparent, all these are examples of transparency and sometimes in that you will notice like, whoa. There's something wrong. This is just a wrong answer. Oh, that's because AI was not smart enough to figure out that Rachel's maiden surname is not there in the database anymore.

Her married name is there. Like how will AI know that? So it's trying to find Rachel Bogner while, all that kind of stuff. So yeah, I don't think so. We should assume the key is to architecturally design for transparency. And if you design for transparency, then trust follows. Even if the transparency will show you AI is making a mistake, you will actually be like, good, I found you were making a mistake.

That's actually a good thing. The idea is not to be perfect. The idea is to be human. 

Richie Cotton: Yeah I love that. And I think that's maybe a common misconception about a lot of these AI applications. People think it's just like one LLM doing everything, but actually you're gonna have a bit of deterministic software there.

You're gonna have other technologies in the LM is only gonna be one part of that. So I love that architectural distinction there. Just to wrap up. I always want more people to learn from. Whose work are you most excited about at the moment? 

Ketan Karkhanis: I'm getting really excited by the work I'm seeing some of my customers do.

I'm really getting excited by what they're doing. They're very inspiring to me. My, some of these users, I give you the example of Macquarie, or I give you the example of EasyJet or I gave you the example of Roche out here. They are fundamentally, or Elon in United Healthcare, they're fundamentally taking the leap into transforming their data experiences.

They're not just thinking. Hey, I will create more charts. They're thinking, how do I drive more growth in my business? And they're solving real problems, which are not just technology problems, but they're solving culture problems. They're solving change management problems. And in many ways, you are not reading those stories in Tech Crunch and on Twitter.

And I know we probably get enamored by this new algorithm was created by this person and da. And yes, that is very cool power to them. Great. Good job. Congratulations. Including my team. But I'll tell you, the real work is happening by, by these data teams everywhere. And I wish I could write a TechCrunch article every week on them or a Bloomberg article, but I'm quite inspired by them.

I'm quite inspired by my ThoughtSpot champions in my community. And it's every day is a new day. Every day is a new day, and we just look forward to having a very exciting time because. We are liberating the world from legacy visualization and bi and it's quite exciting as we do that.

And I'll tell you, the thing that inspires all of ThoughtSpot is not just our customers, but and maybe we could close on this point, is we are a mission driven company. Our mission is not ai. AI is means to an end. Our mission is to make the world more fact driven. That's our mission, and that's a very inspiring mission and very enduring mission.

And AI and technology is just means to that end, but we have our site set on a bigger price which is to make the world more fact driven. That's why it's fun. 

Richie Cotton: Yeah. It's a wonderful mission and I agree. There's been this sort of shift from, generat Air first came out, everyone was just talking about like the technology, the models and ratitude moving towards how do we do stuff with it?

And it is going mainstream. Yeah. Certainly exciting times. Wonderful. Thank you Caden, for all your time. 

Ketan Karkhanis: Thank you so much. And hope to, hope you're well. Anytime you're in the valley, come by. We are in Mountain View. I would love to host you. 

Richie Cotton: Absolutely. Thank you. 

Ketan Karkhanis: All right, bye. Have a good day.